At present, machines are being equipped with more and more actuators and sensors which serve to drive mobile elements. This relates, in particular, to agricultural machines such as self-propelling harvesting machines and tractors which can be operated with different additional equipment such as harvesting attachments for harvesting machines or towed or attached implements, for example for working the soil, for sowing or for harvesting, for tractors.
In order to provide the operator or a remote location with information about whether all the components of the machine are operating correctly and/or in order to actuate components automatically on the basis of the respective operating state of the machine, it has been proposed to detect the operating state of the machine by means of a plurality of sensors (See, Sebastian Blank, Georg Kormann, Karsten Berns, “A Modular Sensor Fusion Approach for Agricultural Machines,” XXXVI CIOSTA & CIGR Section V Conference, June 2011-Vienna.) The signals of the sensors are approximated by means of a sensor knowledge manager and are fused by means of a fusion module in order to obtain a data quantity which is reduced compared to the individual sensor values but is more precise. The fused data is then subjected to fuzzy classification, and the result of this specification is analyzed using a Hidden Markov Model (HMM) in order finally to obtain information about the respective operating state of the machine. The sensor knowledge manager is fed information about the individual sensors from a sensor knowledge database, which information is required to fuse the sensor data. The fusion model and the Hidden Markov Model are fed further, statistical domain knowledge relating to the configuration of the machine from a commonly used database, which domain knowledge is used, on the one hand, to derive a rule base for the fuzzy classification and, on the other hand, to evaluate the data by means of the Hidden Markov Model.
From the mathematical perspective, the HMMs are an extension of normal Markov chains for overcoming the limitation that every transition state has to correspond to a physical observation. This result limits considerably the cases in which a temporary, probabilistic model can be applied. In order to overcome this problem, the HMM is composed of a structure of two connected statistical processes which are embedded one in the other. The first, non-observable process is a Markov chain with states and transition probabilities, and the second, observable process generates, at every point in time, emissions which are based on the current, non-observable internal (first) state. The second process is used as a means for observing the first process which emulates the very largely unknown structure of the process which is under observation.
The Hidden Markov Model λ, can be described on a standard basis by the following five variables: λ, =λ(S, A, B, π, V). In this context, S is a quantity of states (s1 to sN) of a model, A is a state transition matrix with elements aij, B is an emission probability matrix with elements bjk for the probability that the observation vk is conditioned by the state sj, π is a vector for describing an input state and V is an observation symbol alphabet with elements v1 to vM. On the basis of this concept, a multiplicity of real problems can be handled, which problems are characterized in that their internal mechanisms can in some cases not be observed and therefore only stochastic specifications can be derived from their behaviour. Accordingly, the respective state S of the model is derived from the observation V, for which purpose the input state π and the matrices A and B have to be known and, in particular, constant (invariant over time). For further details, reference is made in this respect to the literature, for example L. R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE Vol. 77, No. 2 (February 1989), page 257 et seq.
In the abovementioned prior art by Blank et al, the state transition matrix A, the emission probability matrix B and the input state π are extracted from the database for statistical domain knowledge. This entails, on the one hand, a relatively high level of expenditure for the initial production of the matrix and, on the other hand, involves a certain degree of susceptibility to errors and/or tolerances of sensors because the latter are not included in the statistical, previously defined emission probability matrix B.